This invention relates to illumination schemes for machine vision applications, particularly when surfaces and objects within the field-of-view of a camera vary in reflectivity. Conventional lighting schemes frequently produce shadows and/or glints (i.e., hot-spots) within scenes that contain curved surfaces or objects that rise above a single two-dimensional plane. These problems are exacerbated when lighting must be confined to a limited number of sources and/or when light sources must be located close to a scene that is being illuminated.
Applications that involve machine vision are becoming increasingly common-place. In part, this has arisen as a result of technological advances in the electronics and software development industries, and decreases in the cost of cameras and information processing units. A small number of examples from the range of machine vision applications include: object identification, distance measurements, food inspection, quality control in assembly lines, reading bar codes, object counting, safety monitoring, and biometrics. Sectors and industries that utilize machine vision include military, medical, security, semiconductor fabrication, manufacturing, robotics, and toys.
Almost all image processing techniques and algorithms are affected if regions within images have inadequate illumination. If illumination levels are too low, the result is an insufficient change in brightness intensities to differentiate boundaries of objects or regional changes in reflectance. Reduced signal or light intensities can also lead to a dominance of detector noise within images. Low signal-to-noise ratios generally lead to images that appear “grainy” and difficult to process.
Conversely, if illumination levels are too high, pixels within the camera or detector become saturated. Once again, fully saturated pixels provide no information about changes in brightness levels for image processing algorithms to differentiate edges or boundaries. In some types of video cameras, saturated pixels can also “bleed over” to elevate the apparent brightness of nearby pixels.
In most cases, the information content is lost in regions of images with too few or too many photons. No amount of image processing can retrieve the missing information. In these cases, illumination in all spatial regions of video images must be improved to generate reliable machine vision applications.
For example, precise measurements of object size involve the detection of the object edges where edges are identified as regions where there are sharp gradients in color or luminance. If the camera's view of the edge of an object is distorted by shadows, then the reliability and accuracy of edge-detection algorithms are degraded.
Machine vision applications that include object identification are particularly sensitive to lighting conditions. Dark corners, color changes due to illumination, luminance changes that result from different angles of surface illumination, shadows, and hot-spots can render an object unrecognizable due to lighting conditions.
The controlled illumination of objects is particularly difficult when illumination sources are confined to be close to the objects being illuminated. This confinement can be due, for example, to a desire to make an apparatus compact and/or to reduce power consumption by confining illumination only to the field-of-view of a camera. Such is the case when illuminating the eye using an apparatus mounted to eyewear or a head-mounted device. Examples of these types of systems or apparatus may be found in U.S. Pat. No. 7,515,054 B2 to William C. Torch, which discloses biosensor, communication, and controller applications facilitated by monitoring eye movements.